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Cerious Scroll: Virtual Scrolling with Dynamic Heights — No GPU Transforms

Virtual scrolling sounds simple until you try to apply it to real-world UIs.

Most libraries work fine when:

  • rows are fixed height
  • content never changes after render
  • GPU transforms are acceptable

But that’s not how production applications behave.

After running into repeated issues with existing solutions, I ended up building Cerious Scroll™ — a performance-first virtual scrolling engine designed specifically for dynamic content, predictable behavior, and bounded memory usage.

This post explains why it exists and the design choices behind it.


The Core Problems I Kept Hitting

In real applications, list content is rarely static:

  • images load asynchronously
  • rows expand or collapse
  • templates change based on user interaction
  • conditional content alters layout after render

Most virtualization approaches fall into one of two categories.


1. Pre-calculated Height Models

These assume you can measure everything up front.

They work until:

  • content changes after render
  • async data arrives
  • rows expand dynamically

Once that happens, the scroll model drifts and glitches appear.


2. GPU / Transform-Based Scrolling

Using translate3d, will-change, or compositor-driven positioning can improve raw performance, but introduces other problems:

  • blurry text (especially noticeable in Safari)
  • odd behavior in VDI / RDP environments
  • accessibility quirks
  • browser-specific edge cases

For many enterprise and long-lived apps, predictability matters more than raw benchmark numbers.


The Approach Behind Cerious Scroll™

Cerious Scroll was built around two strict constraints.


🔹 True Dynamic / Variable Heights

Row heights are:

  • measured on demand
  • updated incrementally as content changes
  • allowed to change at runtime

There is no assumption that heights are known ahead of time.

This makes it possible to handle:

  • async content (images, data hydration)
  • expandable rows
  • conditional templates
  • real-world UI behavior without breaking the scroll model

🔹 No GPU Transforms

Cerious Scroll uses pure DOM layout and element-based positioning.

It intentionally avoids:

  • translate3d
  • compositor tricks
  • forced GPU usage

This leads to:

  • clearer text
  • more predictable behavior across browsers
  • better compatibility with Safari, embedded WebViews, and remote desktop environments

Performance Characteristics

Despite avoiding GPU transforms, the design keeps performance tightly bounded:

  • O(1) constant memory usage, independent of dataset size
  • Smooth, predictable scroll behavior targeting 60+ FPS
  • No unbounded metadata growth as lists scale

The engine has been tested with datasets in the tens to hundreds of millions of items without memory growth proportional to dataset size.


Framework-Agnostic by Design

Cerious Scroll is not tied to any specific framework.

It can be integrated with:

  • Vanilla JavaScript
  • Angular
  • React
  • Vue
  • or any framework capable of rendering DOM elements

The engine focuses solely on viewport calculation and positioning — rendering is left to the consumer.


Licensing

Cerious Scroll™ is dual-licensed:

  • GPL v3.0 (GPL-3.0 only) for open-source use
  • Commercial license for proprietary, closed-source, or OEM use

This allows open experimentation while keeping a clear path for commercial and enterprise adoption.


Patent Status

Cerious Scroll includes technology covered by a U.S. provisional patent, filed October 2025 by Cerious DevTech LLC.


Source Code

The project is fully open-source on GitHub:

👉 https://github.com/ceriousdevtech/cerious-scroll

If you’re interested in:

  • virtualization edge cases
  • dynamic-height rendering
  • DOM-first performance tradeoffs

…I’d genuinely love feedback, critique, and discussion.


Closing Thoughts

Cerious Scroll isn’t trying to be a drop-in replacement for every virtual scroller.

It exists because:

  • dynamic content breaks most models
  • GPU-based scrolling isn’t always acceptable
  • memory behavior matters at scale

If you’ve run into similar problems, I’m curious how you approached them — and what tradeoffs you were forced to accept.

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